Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment

Abstract

CT scanning has been widely used for diagnosis, staging and follow-up studies of pulmonary nodules, where image registration plays an essential role in follow-up assessment of CT images. However, it is challenging to align subtle structures in the lung CTs often with large deformation. Unsupervised learning-based registration methods, optimized according to the image similarity metrics, become popular in recent years due to their efficiency and robustness. In this work, we consider segmented tissues, i.e., airways, lobules, and pulmonary vessel structures, in a hierarchical way and propose a multi-stage registration workflow to predict deformation fields. The proposed workflow consists of two registration networks. The first network is the label alignment network, used to align the given segmentations. The second network is the vessel alignment network, used to further predict deformation fields to register vessels in lungs. By combining these two networks, we can register lung CT images not only in the semantic level but also in the texture level. In experiments, we evaluated the proposed algorithm on lung CT images for clinical follow-ups. The results indicate that our method has better performance especially in aligning critical structures such as airways and vessel branches in the lung, compared to the existing methods.